I work as a Quant for an Asset Management and Insurance company and have recently enrolled for Masters degree in Computer Science. I am thinking about investigating how important having a "good" optimizer is for portfolio optimization / asset allocation problems in the presence of,
- Multiple asset classes + multiple risk factors
- Different risk-adjusted-return objective functions,
- Linear constraints as well as VaR or CVaR constraints, and
- Noise resulting from Monte Carlo Methods and Stochastic Processes
My hypothesis is that as you add more 'complexity' to the optimization problem, more traditional local optimization algorithms will struggle to optimize the problem and that it may make more sense to use a global optimization algorithm. It's really just a hypothesis at this point, so I may be totally wrong.
My question is really two fold, firstly, do you think that this is a worthwhile topic to research, and secondly, can anybody recommend any papers which relates to the above topic(s)? Thanks in advance.
P.S. I have read looked over the following questions and answers: